Clean Data Xls
- 1 installs
- Updated March 13, 2026
- longbridge/financial-services-plugins
Cleans messy spreadsheet data by trimming whitespace, fixing casing, converting text-numbers, standardizing dates, deduping, and flagging mixed-type columns.
About
Normalizes and repairs messy spreadsheet data before analysis, handling whitespace, casing, number-as-text, dates, and duplicates. A developer uses it to prep a sheet whose data is inconsistent or dirty.
- Converts numbers-stored-as-text and standardizes dates
- Removes duplicates and flags mixed-type columns
Clean Data Xls by the numbers
- 1 all-time installs (skills.sh)
- Ranked #565 of 688 Office & Documents skills by installs in the Skillselion catalog
- Data as of Aug 2, 2026 (Skillselion catalog sync)
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| Installs | 1 |
|---|---|
| Last updated | March 13, 2026 |
| Repository | longbridge/financial-services-plugins ↗ |
What it does
Cleans messy spreadsheet data by trimming whitespace, fixing casing, converting text-numbers, standardizing dates, deduping, and flagging mixed-type columns.
Files
Clean Data
Clean messy data in the active sheet or a specified range.
Environment
- If running inside Excel (Office Add-in / Office JS): Use Office JS directly (
Excel.run(async (context) => {...})). Read viarange.values, write helper-column formulas viarange.formulas = [["=TRIM(A2)"]]. The in-place vs helper-column decision still applies. - If operating on a standalone .xlsx file: Use Python/openpyxl.
Workflow
Step 1: Scope
- If a range is given (e.g.
A1:F200), use it - Otherwise use the full used range of the active sheet
- Profile each column: detect its dominant type (text / number / date) and identify outliers
Step 2: Detect issues
| Issue | What to look for |
|---|---|
| Whitespace | leading/trailing spaces, double spaces |
| Casing | inconsistent casing in categorical columns (usa / USA / Usa) |
| Number-as-text | numeric values stored as text; stray $, ,, % in number cells |
| Dates | mixed formats in the same column (3/8/26, 2026-03-08, March 8 2026) |
| Duplicates | exact-duplicate rows and near-duplicates (case/whitespace differences) |
| Blanks | empty cells in otherwise-populated columns |
| Mixed types | a column that's 98% numbers but has 3 text entries |
| Encoding | mojibake (é, ’), non-printing characters |
| Errors | #REF!, #N/A, #VALUE!, #DIV/0! |
Step 3: Propose fixes
Show a summary table before changing anything:
| Column | Issue | Count | Proposed Fix |
|---|
Step 4: Apply
- Prefer formulas over hardcoded cleaned values — where the cleaned output can be expressed as a formula (e.g.
=TRIM(A2),=VALUE(SUBSTITUTE(B2,"$","")),=UPPER(C2),=DATEVALUE(D2)), write the formula in an adjacent helper column rather than computing the result in Python and overwriting the original. This keeps the transformation transparent and auditable. - Only overwrite in place with computed values when the user explicitly asks for it, or when no sensible formula equivalent exists (e.g. encoding/mojibake repair)
- For destructive operations (removing duplicates, filling blanks, overwriting originals), confirm with the user first
- After each category of fix (whitespace → casing → number conversion → dates → dedup), show the user a sample of what changed and get confirmation before moving to the next category
- Report a before/after summary of what changed
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